Finding Quantum Critical Points with Neural-Network Quantum States

Understand

Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature.

• However, quantum many-body systems are notoriously hard to study because the dimension of their Hilbert space increases exponentially with their size.
• Recently, machine learning tools known as neural-network quantum states have been shown to effectively and efficiently simulate quantum many-body systems.
• We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states, analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning.